Clearly a mission president effect is observed, note the color changes and regression lines by mission president for each mission.
However we should confirm that the mission president effect isn’t overly influenced/confounded by the number of missionaries. The plot below confirms that the mission president has a potentially strong effect on the number of new investigators within a mission.
Again a mission president effect is observed.
While I was skeptical of any mission president effect on sacrament meeting attendance it appears such an effect is real.
Outliers are possible and we would want to be careful about their effect on predictions/goals. Outliers I noticed include:
raw %>% filter(mission_name == "J") %>% pull(sa_reported) %>% max() when a typical week would be 116. Again it may be worth a quick call or email to the mission president to confirm the blessed nature of such an event.Like Tableau, trelliscope is one R package that enables the viewer to interact visually with the data. Because trelliscope is built for use in R, we can leverage other important tools to mimic Tableau’s interactivity and go well beyond Tableau’s limits by moving visually through various slices of the data using features (cognostics) derived directly from the data. The example below should help illustrate the advantages of such an approach, thus enabling the viewer to observe the data in ways they can’t easily reach using other BI tools, like Tableau.